ggplot2 allows you to:

1. Think about your graphics via layers
2. Apply an organized grammar of graphics for visualization development
3. …

Syntax for ggplot2 visualizations will look like:

library(ggplot2)

ggplot(data = mpg, aes(x = displ, y = hwy)) +
        geom_point(aes(color = class)) +
        xlab("Engine Displacement (liters)") +
        ylab("Highway Mileage") +
        ggtitle("Fuel Economy vs. Engine Displacement") +
        theme_bw()

Load these packages to follow along with this tutorial

library(ggplot2)
library(readxl)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Import the following data sets from the data folder

supermarket <- read_excel("data/Supermarket Transactions.xlsx", sheet = "Data")
facebook <- read.delim("data/facebook.tsv")
reddit <- read.csv("data/reddit.csv")
race <- read.csv("data/race-comparison.csv")

ggplot(data = supermarket)
ggplot(data = supermarket, aes(x = `Purchase Date`, y = Revenue))

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

To display the data we need to tell ggplot what to draw

Check out all the available geoms at docs.ggplot2.org/current

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_histogram()

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_freqpoly()

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_density()

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_histogram(bins = 100, color = "grey40", fill = "white")

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_freqpoly(bins = 100, color = "blue")

ggplot(data = supermarket, aes(x = Revenue)) +
        geom_density(fill = "red", alpha = .5)

ggplot(data = supermarket, aes(x = `Product Family`)) +
        geom_bar()

summary <- supermarket %>%
        group_by(`Product Family`) %>%
        tally()

ggplot(data = summary, aes(x = `Product Family`, y = n)) +
        geom_bar(stat = "identity")

ggplot(data = supermarket, aes(x = `Product Family`)) +
        geom_bar(fill = "dodgerblue", color = "grey40")

ggplot(data = supermarket, aes(x = `Product Family`)) +
        geom_bar(fill = "dodgerblue", color = "grey40", width = .75)

ggplot(data = supermarket, aes(x = `Product Family`)) +
        geom_bar(fill = "dodgerblue", color = "grey40", width = .99)

1. Assess the distribution of age, tenure, and gender in the facebook data.

2. Assess the frequency of age range, education, and income range in the reddit data.

ggplot(supermarket, aes(`Purchase Date`, Revenue)) +
        geom_point()

ggplot(supermarket, aes(`Purchase Date`, Revenue)) +
        geom_point(colour = "blue", size = 1, shape = 5)

ggplot(supermarket, aes(`Purchase Date`, Revenue)) +
        geom_point(colour = "blue", alpha = .25)

ggplot(supermarket, aes(factor(`Units Sold`), Revenue)) +
        geom_point()

ggplot(supermarket, aes(factor(`Units Sold`), Revenue)) +
        geom_jitter(size = 1)

ggplot(supermarket, aes(factor(`Units Sold`), Revenue)) +
        geom_jitter(size = 1, alpha = .1)

sales_by_date <- supermarket %>%
        group_by(`Purchase Date`) %>%
        summarise(Revenue = sum(Revenue, na.rm = TRUE))

ggplot(sales_by_date, aes(`Purchase Date`, Revenue)) +
        geom_line()

sales_plot <- ggplot(sales_by_date, aes(`Purchase Date`, Revenue)) +
        geom_line()

sales_plot + geom_smooth(span = .1)

sales_plot + geom_smooth(span = .9. se = FALSE)

sales_plot + geom_smooth(method = "lm", se = FALSE)

ggplot(supermarket, aes(factor(Children), Revenue)) +
        geom_boxplot()

ggplot(supermarket, aes(factor(Children), Revenue)) +
        geom_boxplot(notch = TRUE, fill = "blue", alpha = .25)

ggplot(supermarket, aes(factor(Children), Revenue)) +
        geom_boxplot(outlier.color = "red", outlier.shape = 1)

Useful for smaller data sets like mpg

ggplot(mpg, aes(class, hwy)) +
        geom_boxplot()

ggplot(mpg, aes(class, hwy)) +
        geom_boxplot() +
        geom_jitter(width = .2, alpha = .5)

ggplot(mpg, aes(class, hwy)) +
        geom_violin()

Bar charts can have a y-axis different than just counts

ggplot(supermarket, aes(x = `Product Family`)) +
        geom_bar()

prod_revenue <- supermarket %>%
        group_by(`Product Family`) %>%
        summarise(Revenue = sum(Revenue, na.rm = TRUE))

ggplot(prod_revenue, aes(x = `Product Family`, y = Revenue)) +
        geom_bar(stat = "identity")

Assess the relationship between tenure and age, gender, likes, etc. in the facebook data.

ggplot(supermarket, aes(Revenue, color = `Product Family`)) +
        geom_freqpoly()

ggplot(data = supermarket, aes(`Product Family`, fill = Gender)) +
        geom_bar(position = "dodge")

ggplot(supermarket, aes(`Purchase Date`, Revenue, color = Country)) +
        geom_point()

prod_revenue <- supermarket %>%
        group_by(`Purchase Date`, `Product Family`) %>%
        summarise(Revenue = sum(Revenue, na.rm = TRUE))

ggplot(prod_revenue, aes(`Purchase Date`, Revenue, color = `Product Family`)) +
        geom_line(alpha = .2) +
        geom_smooth(se = FALSE, span = .1)

ggplot(prod_revenue, aes(`Purchase Date`, Revenue)) +
        geom_line(alpha = .2) +
        geom_smooth(se = FALSE, span = .1) +
        facet_wrap(~ `Product Family`)

ggplot(prod_revenue, aes(`Purchase Date`, Revenue)) +
        geom_blank() +
        facet_grid(.~ `Product Family`)

ggplot(prod_revenue, aes(`Purchase Date`, Revenue)) +
        geom_blank() +
        facet_grid(`Product Family` ~.)